RPI researchers awarded $1.5 million to produce hemp-based insulated siding

Sustainably improving energy efficiency subject of multi-year federal DOE grant

RENSSELAER POLYTECHNIC INSTITUTE

 

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RESEARCHERS FROM RENSSELAER POLYTECHNIC INSTITUTE (RPI) WILL USE HEMP TO DEVELOP A COMMERCIALLY VIABLE, DURABLE, AND LOW-EMBODIED-CARBON INSULATED SIDING PRODUCT

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CREDIT: RENSSELAER POLYTECHNIC INSTITUTE

Researchers from Rensselaer Polytechnic Institute (RPI) will use hemp to develop a commercially viable, durable, and low-embodied-carbon insulated siding product to address what the U.S. Green Building Council says is a “crucial need for building retrofits to improve energy efficiency and reduce carbon emissions.” 

The three-year, $1.5 million award given as part of the Buildings Energy Efficiency Frontiers and Innovation Technologies (BENEFIT) funding opportunity from the United States Department of Energy (DOE) will support RPI faculty and industry partners in creating Hemp Retrofit Structural Insulated Panel (HeRS), a hemp-based insulated siding system that is designed to lower heating and cooling costs for homeowners, is easy for professionals to install, and will reduce the carbon footprint of the built environment.

 

“Hemp can revolutionize manufacturing in the United States,” said Alexandros Tsamis, the associate director of the Center for Architecture, Science and Ecology (CASE) at RPI and the lead primary investigator (PI) on this project. “By utilizing this versatile and renewable crop in building materials, we can create a building retrofit product that will not only be beneficial for homeowners and the environment but also has the potential to jumpstart regional circular economies throughout the United States based on renewable materials.”

Residential buildings account for 60% of the total built surface area, with over two-thirds of them being single-family homes. To address the need to find a sustainable, cost-efficient, and durable siding solution to insulate existing buildings, the researchers will develop a hemp-based structural insulated panel (SIP) for retrofit applications. HeRS will use a dense mat of hemp wool fibers bonded with a recycled binder that has a similar form factor and attachment method to existing siding materials to promote adoption by builders. It will have a minimum R-5 thermal performance to reduce heating, ventilation, and air conditioning (HVAC) energy use by 15%-25%.

Part of the Seed to City hemp initiative at RPI, the hemp siding stems from previous research from Tsamis and Daniel Walczyk, a professor of mechanical engineering, on the use of natural fibers as a resource for sustainable construction, such as hemp in rebar.

In addition to creating the design and manufacturing plans for HeRS, Hakan Hekimoglu, Ed Palermo, Walczyk, and Arta Yazdanseta, all faculty at Rensselaer and co-PIs on the project, will also analyze the embodied carbon footprint of hemp siding, and conduct a viability study for supply chains that will demonstrate how quantities of hemp fiber produced in the United States can be effectively integrated into the production line of HeRS.

HeRS will also leverage the expertise of industry leaders Durasip, Hempitecture, and Introba to innovate and drive the cost-effective deployment of this clean energy technology.

The BENEFIT funding opportunity from the DOE supports the government-wide approach to the climate crisis by helping buildings to improve their energy efficiency and demand flexibility in a cost-effective and equitable manner. The winning projects were chosen through a competitive selection process that rigorously evaluated applications on their technical merit.

The award is issued through the DOE’s Office of Energy Efficiency and Renewable Energy, which has a mission to accelerate the research, development, demonstration, and deployment of technologies and solutions to equitably transition America to net-zero greenhouse gas emissions economy-wide by no later than 2050, and ensure the clean energy economy benefits all Americans, creating good paying jobs for the American people—especially workers and communities impacted by the energy transition and those historically underserved by the energy system and overburdened by pollution.

About CASE: The Center for Architecture, Science, and Ecology at Rensselaer drives innovation in the built environment through a unique collaboration of faculty, research teams, students, and professional firms. CASE takes an integrated science, engineering and tech-startup inspired approach to advancing building capabilities across architecture, construction, technology, and product supply chains, with the goal of creating the sustainable, resilient, and healthy environments of the future. Partnering with New York City and state leadership at the forefront of domestic climate action policy, CASE is well positioned to establish RPI as a leader in built environment research, education, and industry advancement.

About Rensselaer Polytechnic Institute: Founded in 1824, Rensselaer Polytechnic Institute is America’s first technological research university. Rensselaer encompasses five schools, over 30 research centers, more than 140 academic programs including 25 new programs, and a dynamic community made up of over 6,800 students and 110,000 living alumni. Rensselaer faculty and alumni include upwards of 155 National Academy members, six members of the National Inventors Hall of Fame, six National Medal of Technology winners, six National Medal of Science winners, and a Nobel Prize winner in Physics. With nearly 200 years of experience advancing scientific and technological knowledge, Rensselaer remains focused on addressing global challenges with a spirit of ingenuity and collaboration. To learn more, please visit www.rpi.edu.

DOE Acknowledgement and Disclaimer: This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Building Technologies Office (BTO) Award Number DE-EE0010920. The view expressed herein do not necessarily represent the view of the U.S. Department of Energy or the United States Government.

  

Cracking the pear genome: how students helped unlock a new tool for the pear industry

Peer-Reviewed Publication

HUDSONALPHA INSTITUTE FOR BIOTECHNOLOGY

 

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LOGO FOR THE AMERICAN CAMPUS TREE GENOMES INITIATIVE 

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CREDIT: CATHLEEN SHAW

Pears are big business in the Pacific Northwest US. But did you know that traditional pear breeding has remained largely unchanged for centuries? This slow process is difficult and costly, requiring the long-term commitment of labor, materials, and land-space resources. However, traditional pear breeding might get some help from genomics, thanks to a unique collaboration between students, scientists, and the pear industry fostered through an initiative called the American Campus Tree Genomes (ACTG). 

ACTG was born from two professors’ desire to memorialize Auburn University’s iconic Toomer’s Oak trees that were poisoned during the 2010 Auburn University football season. Their plan: sequence the oak’s DNA and create the first-ever live-oak reference genome. To sweeten the pot, they decided to create a semester-long course so that actual Auburn students could take part in sequencing the Auburn oak trees. 

“ACTG leverages iconic and economically valuable trees to bridge the gap between students and cutting-edge genomics,” says ACTG co-founder Alex Harkess, PhD. “Students collaboratively assemble, analyze, and publish tree genomes in prestigious journals, gaining invaluable experience.”

The first semester was a success despite most of the students having never written a manuscript, performed command line bioinformatics, or engaged in plant genomics molecular work. It sparked the now nationwide ACTG initiative, which was officially founded in 2021 by Alex Harkess, PhD, Faculty Investigator at HudsonAlpha Institute for Biotechnology, and Les Goertzen, PhD, Director of the John D. Freeman Herbarium at Auburn University. Other institutions can replicate the experience using their own campus trees as a springboard for scientific and educational endeavors. 

ACTG is disrupting traditional academic models, offering students a unique entry point into the world of genomic research. The initiative transcends textbook learning, immersing participants in the actual process of assembling, analyzing, and publishing tree genomes in esteemed scientific journals. Students in this course have access to cutting-edge genome sequencing techniques and bioinformatic skills through experts at HudsonAlpha. By working on genuine research projects with tangible outcomes, students gain confidence and experience, shaping their trajectories toward successful careers in the ever-evolving field of genomics.

“This course is a welcoming opportunity for students and trainees to not just interact with a completely new idea but become proficient in it no matter their skill level. I had no previous experience with bioinformatics, and I came out with an entirely new, highly marketable skill set,” says Harrison Estes, an Auburn University ‘23 grad who participated in the pear genome class. He is currently a graduate student at the University of Wisconsin and credits the ACTG class as helping him achieve this goal. 

The emphasis on student participation extends beyond technical training. ACTG actively addresses barriers to STEM entry and persistence, providing valuable opportunities for individuals without access to advanced technologies. The ACTG team seeks out participation from small universities and colleges, community and junior colleges, and HBCUs that lack mature genetics and bioinformatics training pipelines. 

The transformative power of ACTG goes beyond equipping students with invaluable skills and experience. By delving into real-world research projects, ACTG participants translate their knowledge into tangible applications that directly benefit the scientific community and economically important industries.

In the case of the pear industry, a cohort of Auburn students in the ACTG initiative worked with pear experts at Washington State University and USDA ARS to create a high-quality pear genome. The meticulous work of the ACTG students yielded a fully phased chromosome-scale assembly, a significant advancement over previous efforts. 

The d’anjou genome assembly, recently published as a featured article in G3: Genes, Genomes, Genetics, reveals thousands of genomic variants which are of great importance to pear breeding efforts. This high-quality resource unlocks a treasure trove of information for pear breeders. The new genome assembly is also an important tool for studies on the evolution, domestication, and molecular breeding of pear. 

“The ACTG: American Campus Tree Genomes program not only built high-quality genomic resources for a valuable pear cultivar that will ultimately benefit growers and consumers alike, but it educated nearly 20 students and scientists in the needs of the apple and pear industry,” said Ines Hanrahan, PhD, Executive Director, Washington Tree Fruit Research Commission. 

The pear is only one of many important tree species in the ACTG pipeline. Learn more about the American Campus Tree Genomes project here. 

Byline: Sarah Sharman, PhD 

 

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JOURNAL

G3 Genes Genomes Genetics

DOI

10.1093/g3journal/jkae003 

ARTICLE TITLE

A chromosome-scale assembly for ‘d’Anjou’ pear

ARTICLE PUBLICATION DATE

15-Mar-2024

 

Rice breakthrough could make automated dosing systems universal

Synthetic biologists’ hack blood-glucose reaction to create chemotherapy detector

RICE UNIVERSITY

 

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A PROTOTYPE ELECTRONIC SENSOR THAT EMITS CURRENT WHEN IT DETECTS THE ANTICANCER DRUG AFIMOXIFENE. RICE UNIVERSITY SYNTHETIC BIOLOGISTS CREATED THE DEVICE TO DEMONSTRATE A NEW METHOD THAT COULD SLASH THE COSTS OF CREATING WEARABLE MONITORS FOR PRECISION, AUTOMATED DRUG DOSING OF CHEMOTHERAPIES AND OTHER DRUGS. (PHOTO BY JEFF FITLOW/RICE UNIVERSITY)

 

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CREDIT: CREDIT PHOTO TO JEFF FITLOW/RICE UNIVERSITY

by Jade Boyd
Special to Rice News

HOUSTON – (March 15, 2024) – Rice University synthetic biologists have found a way to piggyback on the glucose monitoring technology used in automated insulin dosing systems and make it universally applicable for the monitoring and dosing of virtually any drug.

In a recently published study in Nature Communications, researchers in the lab of Caroline Ajo-Franklin demonstrated the technique by modifying a blood-glucose sensor to detect the anticancer drug afimoxifene , an estrogen inhibitor that patient’s bodies also make after they take the chemotherapy tamoxifen.

By building on mature biosensing technology that’s commercially available at most drug stores for under $20, Ajo-Franklin’s team hopes to speed the development of automated dosing systems for chemotherapies and other drugs as well as other technologies for real-time monitoring of biomarkers in the blood.

“The dream is to have technology similar to what’s available today for monitoring and treating variations in blood glucose, and have that be true for basically any drug,” said Ajo-Franklin, a bioscientist, cancer researcher and director of the Rice Synthetic Biology Institute . “Millions of people use blood-glucose monitors every day. If we can use that same basic technology to monitor other drugs and biomarkers, we could move away from the one-size-fits-all dosing regimes that we’re stuck with today.”

The heart of blood-glucose monitoring technology is a biochemical reaction in which specific proteins bind to glucose molecules and release electrons. Millions of these reactions take place within seconds, creating a small electrical current that is proportional to the amount of glucose in the blood sample.

Rong Cai , a postdoctoral research associate and the lead author of the study, tested more than 400 slightly modified versions of the electron-releasing protein and found a version that reacted with afimoxifene, reducing the current output from the glucose reaction in the blood. This allowed the team to detect the presence of afimoxifene by comparing the current produced by the regular glucose test to the reduced current from the modified test.

To demonstrate the technology in an electronic device, Ajo-Franklin’s team worked with the research group of Rice engineer and materials scientist Rafael Verduzco to create an afimoxifene sensor that emitted a current when the drug was detected.

Ajo-Franklin said her lab is already working on both ways to improve the sensitivity of glucose-based drug tests and methods to rapidly identify glucose-oxidizing proteins that can detect drugs other than afimoxifene.

“The glucometer is the part that’s so well-developed,” Cai said. “While our target is different, it’s just a matter of engineering and changing the protein on the inside. On the outside, everything will still be the same. You can still do the test with a strip or on your arm.”

She said another key feature of the technology is that it produces an electrical output.

“If your signal is electrical, you can read it in your phone, store its data in your phone, send it to the cloud, whatever,” Cai said. “That’s the part, that marriage between electricity and biology, that is very attractive.”

Ajo-Franklin is a professor of biosciences in the Weiss School of Natural Sciences and a CPRIT Scholar in Cancer Research with the Cancer Prevention and Research Institute of Texas (CPRIT). Verduzco is a professor of chemical and biomolecular engineering and of materials science and nanoengineering in the George R. Brown School of Engineering .

The research was supported by CPRIT (RR190063), the National Science Foundation (1828869, 2223678) and the Army Research Office (W911NF-22-1-0239).

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JOURNAL

Nature Communications

GOOD AI

Speaking without vocal cords, thanks to a new AI-assisted wearable device

The adhesive neck patch is the latest advance by UCLA bioengineers in speech technology for people with disabilities

UNIVERSITY OF CALIFORNIA - LOS ANGELES

 

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PHOTO OF PERSON’S NECK WITH THE DEVICE — A BLACK ADHESIVE SQUARE — ATTACHED OUTSIDE THE THROAT.

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CREDIT: JUN CHEN LAB/UCLA

People with voice disorders, including those with pathological vocal cord conditions or who are recovering from laryngeal cancer surgeries, can often find it difficult or impossible to speak. That may soon change.

A team of UCLA engineers has invented a soft, thin, stretchy device measuring just over 1 square inch that can be attached to the skin outside the throat to help people with dysfunctional vocal cords regain their voice function. Their advance is detailed this week in the journal Nature Communications.

The new bioelectric system, developed by Jun Chen, an assistant professor of bioengineering at the UCLA Samueli School of Engineering, and his colleagues, is able to detect movement in a person’s larynx muscles and translate those signals into audible speech with the assistance of machine-learning technology — with nearly 95% accuracy.

The breakthrough is the latest in Chen’s efforts to help those with disabilities. His team previously developed a wearable glove capable of translating American Sign Language into English speech in real time to help users of ASL communicate with those who don’t know how to sign.

The tiny new patch-like device is made up of two components. One, a self-powered sensing component, detects and converts signals generated by muscle movements into high-fidelity, analyzable electrical signals; these electrical signals are then translated into speech signals using a machine-learning algorithm. The other, an actuation component, turns those speech signals into the desired voice expression.  

The two components each contain two layers: a layer of biocompatible silicone compound polydimethylsiloxane, or PDMS, with elastic properties, and a magnetic induction layer made of copper induction coils. Sandwiched between the two components is a fifth layer containing PDMS mixed with micromagnets, which generates a magnetic field.

Utilizing a soft magnetoelastic sensing mechanism developed by Chen’s team in 2021, the device is capable of detecting changes in the magnetic field when it is altered as a result of mechanical forces — in this case, the movement of laryngeal muscles. The embedded serpentine induction coils in the magnetoelastic layers help generate high-fidelity electrical signals for sensing purposes.

Measuring 1.2 inches on each side, the device weighs about 7 grams and is just 0.06 inch thick. With double-sided biocompatible tape, it can easily adhere to an individual’s throat near the location of the vocal cords and can be reused by reapplying tape as needed.

Voice disorders are prevalent across all ages and demographic groups; research has shown that nearly 30% of people will experience at least one such disorder in their lifetime. Yet with therapeutic approaches, such as surgical interventions and voice therapy, voice recovery can stretch from three months to a year, with some invasive techniques requiring a significant period of mandatory postoperative voice rest.

“Existing solutions such as handheld electro-larynx devices and tracheoesophageal- puncture procedures can be inconvenient, invasive or uncomfortable,” said Chen who leads the Wearable Bioelectronics Research Group at UCLA, and has been named one the world’s most highly cited researchers five years in a row. “This new device presents a wearable, non-invasive option capable of assisting patients in communicating during the period before treatment and during the post-treatment recovery period for voice disorders.”

How machine learning enables the wearable tech

In their experiments, the researchers tested the wearable technology on eight healthy adults. They collected data on laryngeal muscle movement and used a machine-learning algorithm to correlate the resulting signals to certain words. They then selected a corresponding output voice signal through the device’s actuation component.

The research team demonstrated the system’s accuracy by having the participants pronounce five sentences — both aloud and voicelessly — including “Hi, Rachel, how are you doing today?” and “I love you!”

The overall prediction accuracy of the model was 94.68%, with the participants’ voice signal amplified by the actuation component, demonstrating that the sensing mechanism recognized their laryngeal movement signal and matched the corresponding sentence the participants wished to say.

Going forward, the research team plans to continue enlarging the vocabulary of the device through machine learning and to test it in people with speech disorders.

Other authors of the paper are UCLA Samueli graduate students Ziyuan Che, Chrystal Duan, Xiao Wan, Jing Xu and Tianqi Zheng — all members of Chen’s lab.

The research was funded by the National Institutes of Health, the U.S. Office of Naval Research, the American Heart Association, Brain & Behavior Research Foundation, the UCLA Clinical and Translational Science Institute, and the UCLA Samueli School of Engineering.

The wearable technology is designed to be flexible enough to move with and capture the activity of laryngeal muscles beneath the skin. 

 

The two components — and five layers — of the device allow it to turn muscle movement into electrical signals which, with the help of machine learning, are ultimtately converted into speech signals and audible vocal expression.

CREDIT

Jun Chen Lab/UCLA

JOURNAL

Nature Communications

DOI

10.1038/s41467-024-45915-7 

METHOD OF RESEARCH

Experimental study

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system

ARTICLE PUBLICATION DATE

12-Mar-2024

COI STATEMENT

A patent has been filed related to this work from the University of California, Los Angeles with US provisional patent application No. 63/176,651.

Smart water: how AI is clearing the waters in urban rivers

Peer-Reviewed Publication

CHINESE SOCIETY FOR ENVIRONMENTAL SCIENCES

 

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GRAPHICAL ABSTRACT.

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CREDIT: ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY

Researchers have developed a new machine learning system to improve the accuracy and efficiency of sewer-river system models. This innovative approach, detailed in their latest publication, promises to significantly reduce parameter calibration time and enhance model precision in predicting urban water pollution.

The complexity of integrating sewer systems and urban rivers into a comprehensive model has long posed challenges due to extensive computational demands and limited monitoring data. Traditional calibration methods fall short in addressing these challenges effectively.

A recent study (https://doi.org/10.1016/j.ese.2023.100320) published in Environmental Science and Ecotechnology (Volume 18, 2024) introduces an advanced machine learning system designed to improve the accuracy and efficiency of sewer-river system modeling. This innovative technique significantly reduces the time required for parameter calibration and enhances the precision of predictions regarding urban water pollution.

At the heart of this breakthrough research is the ingenious combination of two advanced technologies: Ant Colony Optimization (ACO) and Long Short-Term Memory (LSTM) networks, integrated into a machine learning parallel system (MLPS). ACO is inspired by the foraging behavior of ants to find the most efficient paths, applied here to navigate through the complex parameter space of water models. Meanwhile, LSTM networks, a type of recurrent neural network, excel in recognizing patterns in sequential data, making them ideal for understanding the temporal dynamics of pollutants in sewer-river systems. By marrying these technologies, the researchers have crafted an MLPS capable of performing rapid and precise calibrations of sewer-river models. Traditional methods, often cumbersome and time-consuming, can't match the efficiency or the accuracy of this new approach. Specifically, the MLPS drastically reduces calibration times from potentially months to just a few days, without sacrificing the model's ability to predict pollution levels accurately.

Highlights

• A model calibration method is built with model surrogation and algorithm optimization.
• The process-based models and machine learning interact in a unique way.
• The optimization time of the integrated process-based model could be saved by 89.94%.
• The accuracy of complex models can be improved based on limited data.

Dr. Yu Tian, lead author of the study, states, "The integration of Ant Colony Optimization and Long Short-Term Memory algorithms into our machine learning parallel system represents a significant leap forward in environmental management. It allows for rapid, accurate model calibration with limited data, opening new avenues for urban water system planning and pollution control."

MLPS offers a robust solution for the accurate simulation of urban water quality, essential for effective environmental management. Its ability to quickly adapt to new data and scenarios makes it a valuable tool for urban planners and environmental scientists, facilitating the development of targeted pollution control strategies and sustainable water management practices.

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References

DOI

10.1016/j.ese.2023.100320

Original Source URL

https://doi.org/10.1016/j.ese.2023.100320

Funding information

This study was supported by the National Key R&D Program of China (2019YFD1100300) and the Fellowship of China Postdoctoral Science Foundation (2020M681105). The authors also acknowledge the State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (No. 2021TS23).

About Environmental Science and Ecotechnology

Environmental Science and Ecotechnology (ISSN 2666-4984) is an international, peer-reviewed, and open-access journal published by Elsevier. The journal publishes significant views and research across the full spectrum of ecology and environmental sciences, such as climate change, sustainability, biodiversity conservation, environment & health, green catalysis/processing for pollution control, and AI-driven environmental engineering. The latest impact factor of ESE is 12.6, according to the Journal Citation ReportTM 2022.

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JOURNAL

Environmental Science and Ecotechnology

DOI

10.1016/j.ese.2023.100320 

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Machine learning parallel system for integrated process-model calibration and accuracy enhancement in sewer-river system

 

Oregon State researchers take deep dive into how much water is stored in snow

OREGON STATE UNIVERSITY

 

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OSU CIVIL ENGINEERING PROFESSOR DAVID HILL CARRIES A SNOW CORING DEVICE UPHILL NEAR THOMPSON PASS, ALASKA. PHOTO BY RYAN CRUMLEY.

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CREDIT: OSU CIVIL ENGINEERING PROFESSOR DAVID HILL CARRIES A SNOW CORING DEVICE UPHILL NEAR THOMPSON PASS, ALASKA. PHOTO BY RYAN CRUMLEY.

CORVALLIS, Ore. – A heavy snowpack is fun for skiers and sledders, and it also acts like an open-air storage tank that melts away to provide water for drinking, irrigation and other purposes during dry months.

But exactly how much water is held in snowpacks, and for how long?

That information, critical to water managers around the globe, has taken on new clarity thanks to a new, more holistic calculation technique developed by researchers in the Oregon State University College of Engineering.

“Water managers tend to consider a portfolio of infrastructure options – surface water reservoirs, groundwater recharge programs, etc. – to match supply to demand,” OSU’s David Hill said. “Increased understanding of how much water is in snow should allow them to make long-term planning decisions for how to adjust that portfolio.”

The study by Hill, a professor of civil engineering, and doctoral student Christina Aragon looked at nearly four decades of snowpack data. Through their new metric, which they call snow water storage, they identified a 22% drop in how much water is held annually in the mountain snowpacks of the lower 48 states.

“Unlike other widely used metrics that capture snow variables at a single point in time, like maximum snow water equivalent, or describe snow characteristics in terms of time, such as length of snow season, snow water storage is applicable at numerous time and space scales,” Hill said. “It’s really just a cumulative sum, not a maximum value; it’s like adding up the number of miles you drive in a given year, rather than just thinking about the 500 you did on one day for your road trip.”

In addition to introducing a better tool for gauging how much water is in snowpacks over periods of time, the findings are important because of what the new metric revealed about mountain snowpacks, which play an outsized role in the nation’s water storage.

Hill and Aragon note that of all the water stored in the form of snow in the lower 48, 72% of it is in the mountains, though mountains cover just 16% of the total area.

“There are many ways to describe or quantify our snow resources, but some of the traditional measures, such as the April 1st snowpack, increasingly do not tell the full story,” Hill said. “We present a new way of describing snow’s water storage ability that adds deeper understanding and has more applicability in cases where our snowfall is increasingly intermittent or, regrettably, turning to rain.”

The researchers’ work, presented in a paper published in Hydrology and Earth System Sciences, builds on a commonly used measurement known as snow water equivalent; as its name implies, it’s how much water is left in a container after the snow that was placed in it melts.

“By considering the amount of water held in the snowpack and the amount of time the water is stored as snow, we are able to quantify water storage in different types of snowpacks,” Aragon said. “This includes persistent snowpacks, like we typically have at high elevations in the mountains; transient snowpacks, which are typically found at lower elevations; and snowpacks that are transitioning from persistent to transient due to climate warming.”

Aragon adds that because the snow water storage metric can be applied to multiple types of snowpacks, it may become increasingly valuable for monitoring and predicting water resources “amidst a future of increased climate variability.”

Hill points out that the past several years in the lower 48 have seen a “feast or famine cycle of extremes when it has come to the where and the when of our snow and rain.” And in general snowpacks have considerably declined over the past 10 to 20 years.

“That particularly matters in places like Oregon, where 15% of the state’s total annual precipitation falls as snow, and our snowpack functions like a reservoir,” he said. “It holds back winter precipitation and slowly releases it in spring and early summer. This is useful because, at those times, our rainfall has tapered off for the year, but demand for water is on the rise.”

As the climate warms and snowpacks become more and more variable – the winter of 2023-24 is a good example, Hill said – a metric like the new one developed at OSU helps to more objectively quantify the reservoir storage aspect of the globe’s snowpacks.

From local to regional scales, he notes, municipal and agricultural users of water need to balance demand with supply, and snow storage dramatically influences the timing of the supply side.

“As we move forward, and as we have moved from the past to the present, the relatively good news is that annual precipitation amounts tend to not change that dramatically,” he said. “However, changing temperatures greatly influence snow storage and therefore the timing of water availability.”

Funding for the work came from the OSU Graduate School Oregon Lottery Award for Academic Excellence and from the Oregon State Water Resources Graduate Program Alumni Award.

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JOURNAL

Hydrology and Earth System Sciences

DOI

10.5194/hess-28-781-2024 

METHOD OF RESEARCH

Data/statistical analysis

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Changing snow water storage in natural snow reservoirs